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 Duration 21 hours (3 days)

Course Outline

Foundations of X402 and Decentralized AI

  • Introduction to Coinbase’s X402 protocol
  • Rationale: Creating secure AI agents with on-chain identity
  • Architectural overview and key components

Preparing the Development Environment

  • Installation of the X402 SDK and required dependencies
  • Configuration of wallets and identity layers
  • Integrating Node.js and Python for cross-language workflows

Exploring the X402 Protocol

  • Fundamental principles of agent-wallet interactions
  • Data signing, verification processes, and privacy mechanisms
  • Secure communication and authorization models

Incorporating AI Models into X402 Applications

  • Connecting with OpenAI, DeepSeek, Qwen, and Mistral Small
  • Oversight of model inference and token consumption
  • Building autonomous, wallet-aware AI agents

Developing Smart Contracts for AI Interactions

  • Specifying agent permissions in Solidity
  • Processing LLM-driven blockchain transactions
  • Testing and debugging decentralized AI behaviors

Security, Compliance, and Data Sovereignty

  • Regulatory implications for AI and crypto sectors
  • Data ownership rights and privacy-preserving computations
  • Auditing and securing agent interactions

Advanced Architectures and Enterprise-Level Integration

  • Connecting X402 with corporate identity systems
  • Designing scalable, multi-agent infrastructures
  • Case studies: AI-driven payments, analytics, and automation

Deployment and Operational Management

  • Operating decentralized AI agents in production environments
  • Monitoring and maintaining X402-based systems
  • Performance and cost optimization strategies

Conclusion and Future Directions

Requirements

  • Familiarity with blockchain fundamentals
  • Experience in API integration and smart contract development
  • Foundational knowledge of large language models and prompt engineering

Target Audience

  • Software engineers creating AI-integrated blockchain applications
  • Enterprise architects investigating decentralized AI architectures
  • Engineering leads developing secure, compliant AI agents using on-chain systems

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